Association between cluster analysis for multiple measures and International Classification of Diseases 11th revision as classification of chronic pain patients
Bibliographic record
Abstract
Cluster analysis can classify patients with chronic pain using multiple scales, and classification of chronic pain will be adopted in the International Classification of Diseases 11th revision (ICD–11) in 2022. In the present study, we aimed to investigate whether cluster analysis was practical for classifying chronic pain and to determine the association between these two classifications for chronic pain. This study included 229 patients with chronic pain who completed a self–reported questionnaire at the first visit to a pain clinic in a university hospital. Patients were clustered using a two–step cluster analysis (TSCA), a machine learning method, for the scores of nine questionnaires. Thereafter, the proportions of clusters among major and several minor classifications were tested using the analysis of covariance adjusted for age and doctor. The following three clusters were calculated using TSCA: mild, moderate, and severe symptoms. Among the major classifications of chronic pain in ICD–11, the distribution of clusters significantly differed, but the proportions of these three clusters in each chronic pain classification did not differ. Our findings suggested that TSCA for multiple measures may be a better approach for the classification of chronic pain, but its classification is not associated with the classification of chronic pain in ICD–11.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".